Abstract: My research seeks to systematically exploit mechanical dynamics to make future robots faster, more efficient, and more agile then today’s kinematically controlled systems. Drawing inspiration from biology and biomechanics, I design and control robots whose motion emerges in great part passively from the interaction of inertia, gravity, and elastic oscillations. Energy is stored and returned periodically in springs and other dynamic elements, and continuous motion is merely initiated and shaped through the active actuator inputs. In this context, I am particularly interested in questions of gait selection. Should a legged robot use different gaits at different desired speeds? If so, what constitutes these gaits and what causes their existence? How do they relate to gaits observed in biology? We study these questions in conceptual models, in hardware implementations, and through biomechanical experiments. In the long term, this research will allow the development of systems that reach and even exceed the agility of humans and animals. It will enable us to build autonomous robots that can run as fast as a cheetah and as enduring as a husky, while mastering the same terrain as a mountain goat. And it will provide us with novel designs for prosthetics, orthotics, and active exoskeletons that help restoring the locomotion skills of the disabled and can be used as training and rehabilitation devices for the injured.

Biography: C. David Remy, is Assistant Professor of Mechanical Engineering at the University of Michigan, Ann Arbor. He received his Ph.D. from ETH Zurich (Prof. Roland Siegwart), and holds both a M.Sc. in Mechanical Engineering from the University of Wisconsin and a Diploma in Engineering Cybernetics from the University of Stuttgart. Dr. Remy is the head of the Robotics and Motion Laboratory. His research interests include the design, simulation, and control of legged robots, exoskeletons, and other nonlinear systems. Drawing inspiration from biology and biomechanics, he is particularly interested in the effects and exploitation of natural dynamic motions, the role of different gaits, and the possibility of force/torque controllable systems; both in conceptual models and in hardware realizations

Abstract: We develop a general control framework where a low-level optimizer is built into the robot dynamics. This optimizer together with the robot constitute a goal-directed dynamical system, which is controlled on a higher level. The high-level command is a cost function. It can encode desired accelerations, end-effector poses, center of pressure, and many other intuitive features that have been studied before. Unlike the currently popular quadratic programming framework, which comes with performance guarantees at the expense of modeling flexibility, the optimization problem we solve at each time step is non-convex and non-smooth. Nevertheless, by exploiting the unique properties of the soft-constraint physics model we have recently developed, we design an efficient optimizer for goal-directed dynamics. This new computational infrastructure can facilitate tele-operation, feature-based control, deep learning of control policies, and trajectory optimization. It will become a standard feature in future releases of the MuJoCo simulator.

Abstract: In this talk, I will discuss research I conducted for my PhD thesis on how embodied agents--both virtual agents and physical robots--can achieve positive social and communicative outcomes through the use of situated gaze mechanisms. I will discuss why social gaze is one of the most important nonverbal cues to consider for interactive embodied agents and present several projects I have carried out to design and test models of gaze behavior for agents in various contexts. These projects include (1) how agents can produce gaze shifts that target specific high-level interaction outcomes, (2) how agents can effectively utilize gaze aversions in conversation, (3) how agents can coordinate their gaze with the user’s gaze while collaborating on a physical task, and (4) how agents can adapt their gaze behaviors to the personality of their users for rehabilitation. I will also discuss current research directions into situated interaction with robots that I am now pursuing at Microsoft Research.

Biography: Sean Andrist is a researcher at Microsoft Research in the Adaptive Systems and Interaction group. His research interests involve designing, building, and evaluating socially interactive technologies that are physically situated in the open world. He recently received his PhD from the Department of Computer Sciences at the University of Wisconsin–Madison, where he conducted research on gaze mechanisms for the development of communicative characters, including both embodied virtual agents and social robots.

Abstract: Getting a small unmanned aircraft to fly aggressively and autonomously through an unknown, cluttered environment creates substantial challenges for the vehicle's navigation and control. Without a prior map, the vehicle has to detect obstacles and avoid them, often on the basis of little sensor data, and make rapid decisions about how to move around given an uncertain and incomplete model of the world and the vehicle's position. I will discuss some recent results from my group in developing approximate inference and planning algorithms that have enabled fast and aggressive autonomous motion for unmanned vehicles in the air and on the ground.

Biography: Nicholas Roy is an Associate Professor in the Department of Aeronautics & Astronautics at the Massachusetts Institute of Technology and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. He received his Ph. D. in Robotics from Carnegie Mellon University in 2003. His research interests include unmanned aerial vehicles, autonomous systems, human-computer interaction, decision-making under uncertainty and machine learning. He spent two years at Google [x] as the founder of Project Wing.